@article{landis1977measurement,
  title={The measurement of observer agreement for categorical data},
  author={Landis, J Richard and Koch, Gary G},
  journal={biometrics},
  volume={33},
  number={1},
  pages={159--174},
  year={1977},
  publisher={JSTOR}
}

@article{hasler1969cultural,
  title={Cultural eutrophication is reversible},
  author={Hasler, Arthur D},
  journal={BioScience},
  volume={19},
  number={5},
  pages={425--431},
  year={1969},
  publisher={Oxford University Press}
}

@article{smith1999eutrophication,
  title={Eutrophication: impacts of excess nutrient inputs on freshwater, marine, and terrestrial ecosystems},
  author={Smith, Val H and Tilman, G David and Nekola, Jeffery C},
  journal={Environmental pollution},
  volume={100},
  number={1},
  pages={179--196},
  year={1999},
  publisher={Elsevier}
}

@book{schindler2008algal,
  title={The algal bowl: overfertilization of the world's freshwaters and estuaries},
  author={Schindler, David W and Vallentyne, John R},
  year={2008},
  publisher={University of Alberta Press Edmonton},
  pages={334}
}

@article{downing2001predicting,
  title={Predicting cyanobacteria dominance in lakes},
  author={Downing, John A and Watson, Susan B and McCauley, Edward},
  journal={Canadian journal of fisheries and aquatic sciences},
  volume={58},
  number={10},
  pages={1905--1908},
  year={2001},
  publisher={NRC Research Press}
}

@incollection{paerl2006ecology,
  title={Ecology of harmful cyanobacteria},
  author={Paerl, HW and Fulton III, RS},
  booktitle={Ecology of harmful algae},
  pages={95--109},
  year={2006},
  publisher={Springer}
}

@article{hubert1985comparing,
  title={Comparing partitions},
  author={Hubert, Lawrence and Arabie, Phipps},
  journal={Journal of classification},
  volume={2},
  number={1},
  pages={193--218},
  year={1985},
  publisher={Springer}
}

@article{cohen1960coefficient,
  title={A coefficient of agreement for nominal scales.},
  author={Cohen, Jacob},
  journal={Educational and Psychological Measurement},
  volume={20},
  pages={37--46},
  year={1960}
}

@article{cohen1968weighted,
  title={Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit.},
  author={Cohen, Jacob},
  journal={Psychological bulletin},
  volume={70},
  number={4},
  pages={213},
  year={1968},
  publisher={American Psychological Association}
}

@article{yuan2014managing,
  title={Managing microcystin: identifying national-scale thresholds for total nitrogen and chlorophyll a},
  author={Yuan, Lester L and Pollard, Amina I and Pather, Santhiska and Oliver, Jacques L and D'Anglada, Lesley},
  journal={Freshwater Biology},
  volume={59},
  number={9},
  pages={1970--1981},
  year={2014},
  publisher={Wiley Online Library}
}

@article{kasinak2014benchtop,
  title={Benchtop fluorometry of phycocyanin as a rapid approach for estimating cyanobacterial biovolume},
  author={Kasinak, Jo-Marie E and Holt, Brittany M and Chislock, Michael F and Wilson, Alan E},
  journal={Journal of Plankton Research},
  pages={fbu096},
  year={2014},
  publisher={Oxford Univ Press}
}

@article{xian2009updating,
  title={Updating the 2001 National Land Cover Database land cover classification to 2006 by using Landsat imagery change detection methods},
  author={Xian, George and Homer, Collin and Fry, Joyce},
  journal={Remote Sensing of Environment},
  volume={113},
  number={6},
  pages={1133--1147},
  year={2009},
  publisher={Elsevier}
}

@article{homer2004development,
  title={Development of a 2001 national land-cover database for the United States},
  author={Homer, Collin and Huang, Chengquan and Yang, Limin and Wylie, Bruce and Coan, Michael},
  journal={Photogrammetric Engineering \& Remote Sensing},
  volume={70},
  number={7},
  pages={829--840},
  year={2004},
  publisher={American Society for Photogrammetry and Remote Sensing}
}

@Manual{lakemorpho2014,
  title = {lakemorpho: Lake morphometry in {R}. {R} package version 1.0. http://CRAN.R-project.org/package=lakemorpho},
  author = {Hollister, Jeffrey W.},
  year = {2014},
  note = {R package version 1.0},
  url = {http://CRAN.R-project.org/package=lakemorpho},
  }

@Manual{LakeTrophicModelling,
    title = {LakeTrophicModelling: Package to reproduce {H}ollister et al. (2014) {M}odeling Lake Trophic State: A Data Mining Approach.},
    author = {Jeffrey W. Hollister and W. Bryan Milstead and Betty J. Kreakie},
    note = {R package version 0.1},
    url = {https://github.com/USEPA/LakeTrophicModelling},
    year = {2014}
  }

@article{NLMDInPrep,
  title={National Lake Morphometry Dataset V1.0},
  author={Hollister,Jeffrey W and Milstead, W Bryan},
  year={In Preparation}
}

@article{beaulieu2013nutrients,
  title={Nutrients and water temperature are significant predictors of cyanobacterial biomass in a 1147 lakes data set},
  author={Beaulieu, Marieke and Pick, Frances and Gregory-Eaves, Irene},
  journal={Limnol. Oceanogr},
  volume={58},
  number={5},
  pages={1736--1746},
  year={2013}
}

@article{hollister2010volume,
  title={Using GIS to estimate lake volume from limited data},
  author={Hollister, Jeffrey and Milstead, W Bryan},
  journal={Lake and Reservoir Management},
  volume={26},
  number={3},
  pages={194--199},
  year={2010},
  publisher={Taylor \& Francis}
}

@Manual{diaz-uriarte2010varSelRF,
    title = {varSelRF: Variable selection using random forests. {R} package version 0.7-3. http://CRAN.R-project.org/package=varSelRF},
    author = {Ramon Diaz-Uriarte},
    year = {2010},
    note = {R package version 0.7-3},
    url = {http://CRAN.R-project.org/package=varSelRF}
  }

@article{lu_environmental_2013,
	title = {Environmental factors influencing cyanobacteria community structure in Dongping Lake, China},
	volume = {25},
	issn = {10010742},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S1001074212602976},
	doi = {10.1016/S1001-0742(12)60297-6},
	number = {11},
	urldate = {2013-11-22},
	journal = {Journal of Environmental Sciences},
	author = {Lu, Xuetang and Tian, Chang and Pei, Haiyan and Hu, Wenrong and Xie, Jun},
	month = nov,
	year = {2013},
	pages = {2196--2206}
}

@article{merel_state_2013,
	title = {State of knowledge and concerns on cyanobacterial blooms and cyanotoxins},
	volume = {59},
	issn = {01604120},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0160412013001311},
	doi = {10.1016/j.envint.2013.06.013},
	urldate = {2013-09-03},
	journal = {Environment International},
	author = {Merel, Sylvain and Walker, David and Chicana, Ruth and Snyder, Shane and Baurès, Estelle and Thomas, Olivier},
	month = sep,
	year = {2013},
	pages = {303--327}
}

@Manual{hollister2013lakemorpho,
    title = {lakemorpho: Lake morphometry in R},
    author = {Jeffrey Hollister},
    year = {2013},
    note = {R package version 1.0},
    url = {http://www.github.com/USEPA/lakemorpho},
  }

@article{diaz2006gene,
  title={Gene selection and classification of microarray data using random forest},
  author={D{\'\i}az-Uriarte, Ram{\'o}n and De Andres, Sara Alvarez},
  journal={BMC bioinformatics},
  volume={7},
  number={1},
  pages={3},
  year={2006},
  publisher={BioMed Central Ltd}
}

@article{berman_who_2013,
	title = {Who Will Pay for Public Access to Research Data?},	volume = {341},
	issn = {0036-8075, 1095-9203},
	url = {http://www.sciencemag.org/content/341/6146/616},
	doi = {10.1126/science.1241625},
	language = {en},
	number = {6146},
	urldate = {2013-08-13},
	journal = {Science},
	author = {Berman, Francine and Cerf, Vint},
	month = aug,
	year = {2013},
	note = {{PMID:} 23929969},
	pages = {616--617}
}

@book{fekedulegn_coping_2002,
	title = {Coping with multicollinearity: An example on application of principal components regression in dendroecology},
	publisher = {{US} Department of Agriculture, Forest Service, Northeastern Research Station},
	author = {Fekedulegn, B Desta and Colbert, {JJ} and Hicks Jr, {RR} and Schuckers, Michael E},
	year = {2002}
}

@misc{usepa_national_2005,
	title = {National Hydrography Dataset Plus - {NHD} Plus},
	url = {http://www.horizon-systems.com/NHDPlus/NHDPlusV1_home.php},
	urldate = {2013-07-09},
	author = {{USEPA} and {USGS}},
	year = {2005}
}

@article{naes_understanding_2001,
	title = {Understanding the collinearity problem in regression and discriminant analysis},
	volume = {15},
	copyright = {Copyright © 2001 John Wiley \& Sons, Ltd.},
	issn = {1099-{128X}},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/cem.676/abstract},
	doi = {10.1002/cem.676},
	abstract = {This paper presents a discussion of the collinearity problem in regression and discriminant analysis. The paper describes reasons why the collinearity is a problem for the prediction ability and classification ability of the classical methods. The discussion is based on established formulae for prediction errors. Special emphasis is put on differences and similarities between regression and classification. Some typical ways of handling the collinearity problems based on {PCA} will be described. The theoretical discussion will be accompanied by empirical illustrations. Copyright © 2001 John Wiley \& Sons, Ltd.},
	language = {en},
	number = {4},
	urldate = {2013-06-28},
	journal = {Journal of Chemometrics},
	author = {Næs, Tormod and Mevik, Bjørn-Helge},
	year = {2001},
	keywords = {{PCA}, {PCR}, classification, collinearity, discriminant analysis, regression},
	pages = {413–426}
}

@article{hollister2010volume,
  title={Using {G}IS to estimate lake volume from limited data},
  author={Hollister, Jeffrey and Milstead, W Bryan},
  journal={Lake and Reservoir Management},
  volume={26},
  number={3},
  pages={194--199},
  year={2010},
  publisher={Taylor \& Francis}
}

@article{hollister_predicting_2011,
	title = {Predicting Maximum Lake Depth from Surrounding Topography},
	volume = {6},
	url = {https://doi.org/10.1371/journal.pone.0025764},
	doi = {10.1371/journal.pone.0025764},
	abstract = {Information about lake morphometry (e.g., depth, volume, size, etc.) aids understanding of the physical and ecological dynamics of lakes, yet is often not readily available. The data needed to calculate measures of lake morphometry, particularly lake depth, are usually collected on a lake-by-lake basis and are difficult to obtain across broad regions. To span the gap between studies of individual lakes where detailed data exist and regional studies where access to useful data on lake depth is unavailable, we developed a method to predict maximum lake depth from the slope of the topography surrounding a lake. We use the National Elevation Dataset and the National Hydrography Dataset – Plus to estimate the percent slope of surrounding lakes and use this information to predict maximum lake depth. We also use field measured maximum lake depths from the {US} {EPA's} National Lakes Assessment to empirically adjust and cross-validate our predictions. We were able to predict maximum depth for ∼28,000 lakes in the Northeastern United States with an average cross-validated {RMSE} of 5.95 m and 5.09 m and average correlation of 0.82 and 0.69 for Hydrological Unit Code Regions 01 and 02, respectively. The depth predictions and the scripts are openly available as supplements to this manuscript.},
	number = {9},
	urldate = {2013-06-28},
	journal = {{PLoS} {ONE}},
	author = {Hollister, Jeffrey W. and Milstead, W. Bryan and Urrutia, M. Andrea},
	month = sep,
	year = {2011},
	pages = {e25764}
}

@article{lun_relationship_2002,
	title = {Relationship between microcystin in drinking water and colorectal cancer},
	volume = {15},
	url = {http://www.chinacdc.cn:87/freeArticles/pastIssues/2002/No2/200702/P0200702276986786817183120021528758.pdf},
	urldate = {2013-06-27},
	journal = {Biomedical and environmental sciences},
	author = {Lun, Zhou and Hai, Yu and Kun, Chen},
	year = {2002},
	pages = {166–171}
}

@article{uusitalo_advantages_2007,
	title = {Advantages and challenges of Bayesian networks in environmental modelling},
	volume = {203},
	issn = {03043800},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0304380006006089},
	doi = {10.1016/j.ecolmodel.2006.11.033},
	number = {3-4},
	urldate = {2013-06-27},
	journal = {Ecological Modelling},
	author = {Uusitalo, Laura},
	month = may,
	year = {2007},
	pages = {312--318}
}

@article{soranno_factors_1997,
	title = {Factors affecting the timing of surface scums and epilimnetic blooms of blue-green algae in a eutrophic lake},
	volume = {54},
	url = {http://www.nrcresearchpress.com/doi/pdf/10.1139/f97-104},
	number = {9},
	urldate = {2013-06-27},
	journal = {Canadian Journal of Fisheries and Aquatic Sciences},
	author = {Soranno, P. A.},
	year = {1997},
	pages = {1965–1975}
}

@article{soranno_lake_2009,
	title = {The lake landscape-context framework: linking aquatic connections, terrestrial features and human effects at multiple spatial scales},
	volume = {30},
	shorttitle = {The lake landscape-context framework},
	url = {http://www.fw.msu.edu/~llrg/publications/Soranno_etal_SIL30_2009.pdf},
	urldate = {2013-06-27},
	journal = {Verhandlungen der Internationalen Vereinigung für theoretische und angewandte Limnologie},
	author = {Soranno, Patricia A. and Webster, Katherine E. and Cheruvelil, Kendra S. and Bremigan, Mary T.},
	year = {2009},
	pages = {695–700}
}

@article{lewis_application_2008,
	title = {Application of a nutrient-saturation concept to the control of algal growth in lakes},
	volume = {24},
	issn = {1040-2381},
	url = {http://www.tandfonline.com/doi/abs/10.1080/07438140809354049},
	doi = {10.1080/07438140809354049},
	abstract = {Abstract Either phosphorus or nitrogen can be responsible for nutrient limitation of algae in lakes. Nitrogen limitation can be defeated by heterocystous cyanobacteria through nitrogen fixation, but no comparable mechanism exists for P. Therefore, P is considered the predominant factor limiting phytoplankton biomass in lakes. Even so, increasing numbers of studies show that many lakes are limited by N deficiency because heterocystous cyanobacteria do not become sufficiently abundant to offset N deficiency. Where N limitation prevails, P control over phytoplankton populations can be achieved only if P concentrations are first reduced to a saturation threshold that is determined by the amount of available N. The extent of this reduction, which will typically occur without any suppression of phytoplankton biomass, can be estimated from nutrient chemistry, nutrient enrichment experiments, and information on the stoi-chiometry of phytoplankton, as illustrated with data for a Colorado reservoir in which a reduction of N of about 50\% would be necessary to induce P limitation. Analysis based on stoichiometry could allow managers of water quality in lakes to anticipate the implications of N limitation for P-based management of water quality.},
	number = {1},
	urldate = {2013-06-24},
	journal = {Lake and Reservoir Management},
	author = {Lewis, William M. and Saunders, James F. and {McCutchan}, James H.},
	year = {2008},
	pages = {41--46}
}

@article{hiriart-baer_water_2009,
	title = {Water quality trends in Hamilton Harbour: Two decades of change in nutrients and chlorophyll a},
	volume = {35},
	issn = {0380-1330},
	shorttitle = {Water quality trends in Hamilton Harbour},
	url = {http://www.sciencedirect.com/science/article/pii/S0380133009000641},
	doi = {10.1016/j.jglr.2008.12.007},
	abstract = {Systematic water quality research and monitoring has been on-going in Hamilton Harbour since 1987 in response to the Remedial Action Plan ({RAP)} for this Area of Concern ({AOC).} Here we present a spatio-temporal analysis of water quality in the harbour and its biological response from 1987 to 2007. Overall nutrient concentrations have decreased by 16 ({SRP)}, 26 ({NH3-Tot)} and 36\% ({TP)} in the harbour, chl a concentrations have decreased by 16\% and {NO3/2} concentrations have increased by 27\%. Hypoxia in the hypolimnion of Hamilton Harbour remains a common occurrence despite improvements in surface water quality conditions. Seasonal patterns in water quality in Hamilton Harbour are mainly driven by biological activity and show typical patterns observed in dimictic nutrient rich lakes. There is systematic spatial variability in water quality in the harbour which is related to the proximity of point and non-point sources; however, there is coherence among all stations sampled and similar temporal trends were observed for all stations. The biological response in the harbour suggests that phosphorus limited algal growth is becoming more prevalent in Hamilton Harbour and the rate of improvements in water quality should accelerate in the near future following further reductions in phosphorus loadings.},
	number = {2},
	urldate = {2013-06-24},
	journal = {Journal of Great Lakes Research},
	author = {Hiriart-Baer, Véronique P. and Milne, Jacqui and Charlton, Murray N.},
	month = jun,
	year = {2009},
	keywords = {Biological response, Hamilton Harbour, Remediation, Temporal trends, Water quality},
	pages = {293--301}
}

@article{sondergaard_using_2011,
	title = {Using chlorophyll a and cyanobacteria in the ecological classification of lakes},
	volume = {11},
	issn = {1470-{160X}},
	url = {http://www.sciencedirect.com/science/article/pii/S1470160X11000550},
	doi = {10.1016/j.ecolind.2011.03.002},
	abstract = {Phytoplankton is one of the four key biological quality elements to be used in the ecological classification of lakes in Europe according to the Water Framework Directive ({WFD).} Chlorophyll a (Chla) has so far been used as the main – and sometimes only – metric to define class boundaries. Chla is often a key metric for lake managers and is used to determine whether and how much action should be taken to reduce the external nutrient loading. In this paper we present the analyses of empirical relationships between nutrient (total phosphorus, {TP}, total nitrogen, {TN)} concentrations versus Chla and the proportion of cyanobacteria of total phytoplankton biomass based on data from 440 Danish lakes (1800 lake years). These data represent one eco-region sampled using standardised methodology, thereby minimising the heterogeneity often seen in large datasets. Sampling frequency is important for the precision by which Chla can be determined and the precision is always low with less than 15 summer measurements. As expected Chla was related significantly to {TP}, but the variability was high, with R2 reaching only 0.47, 0.59 and 0.61 in shallow, stratified and siliceous lakes, respectively, based on summer averages. The correlation was strongest in late summer (R2 up to 0.80) and weak in winter. Chla is also related to {TN}, but the correlation coefficients were low throughout the year, and in a multiple regression with {TP} included, {TN} only added little to the total variability. Similarly, the proportion of cyanobacteria increased significantly with {TP}, but the correlation was weak. Seasonal and yearly data from five lakes with relatively stable {TP} show considerable variations in Chla and cyanobacteria abundance during a 20-year monitoring period. It is concluded that despite clear nutrient phytoplankton relationships it will be difficult to define the proposed {WFD} ecological classes – particularly regarding cyanobacteria. To ensure a high degree of certainty for meeting a specific water quality threshold, lake managers must reduce the external phosphorus loading more strongly than expected from existing simple empirical external loading-inlake {TP–Chla} relationships.},
	number = {5},
	urldate = {2013-06-24},
	journal = {Ecological Indicators},
	author = {Søndergaard, Martin and Larsen, Søren E. and Jørgensen, Torben B. and Jeppesen, Erik},
	month = sep,
	year = {2011},
	keywords = {Chlorophyll a, Cyanobacteria, Phosphorus, Water framework directive, nitrogen},
	pages = {1403--1412}
}

@article{dimberg_predicting_2013,
	title = {Predicting median monthly chlorophyll-a concentrations},
	volume = {43},
	issn = {0075-9511},
	url = {http://www.sciencedirect.com/science/article/pii/S0075951112000679},
	doi = {10.1016/j.limno.2012.08.011},
	abstract = {Chlorophyll-a (Chl-a) is a plant pigment which is used in many environmental monitoring programs as a water quality indicator for lakes. However, monthly Chl-a data are often lacking in many monitored lakes as measurements are concentrated to certain periods of the year. This study investigates two methods of how monthly Chl-a medians can be predicted (i) new monthly regression models from median summer total phosphorus concentrations and latitude, (ii) and with monthly constants added to regression models from the literature. Data from 308 lakes were used and the trophic status of the lakes ranged from oligotrophic to hypertrophic, they were located from northern Sweden (Europe) to southern Florida (North America). These models may be useful for understanding the general Chl-a seasonality in lakes and for managing lakes in which Chl-a measurements are not made over the whole year.},
	number = {3},
	urldate = {2013-06-24},
	journal = {Limnologica - Ecology and Management of Inland Waters},
	author = {Dimberg, Peter H. and Hytteborn, Julia K. and Bryhn, Andreas C.},
	month = may,
	year = {2013},
	keywords = {Chlorophyll-a, Phosphorus, Regression model, Seasonality, Statistical model, lake},
	pages = {169--176}
}

@article{peretyatko_assessment_2010,
	title = {Assessment of the risk of cyanobacterial bloom occurrence in urban ponds: probabilistic approach},
	volume = {46},
	issn = {0003-4088, 2100-{000X}},
	shorttitle = {Assessment of the risk of cyanobacterial bloom occurrence in urban ponds},
	url = {http://www.limnology-journal.org/10.1051/limn/2010009},
	doi = {10.1051/limn/2010009},
	number = {2},
	urldate = {2013-06-20},
	journal = {Annales de Limnologie - International Journal of Limnology},
	author = {Peretyatko, Anatoly and Teissier, Samuel and Backer, Sylvia De and Triest, Ludwig},
	month = may,
	year = {2010},
	pages = {121--133}
}

@article{molot_nitrogen/phosphorus_1991,
	title = {{Nitrogen/Phosphorus} Ratios and the Prediction of Chlorophyll in Phosphorus-Limited Lakes in Central Ontario},
	volume = {48},
	issn = {0706-{652X}},
	url = {http://www.nrcresearchpress.com/doi/abs/10.1139/f91-019},
	doi = {10.1139/f91-019},
	abstract = {The response of mean ice-free chlorophyll a in 15 stratified, P-limited oligotrophic and mesotrophic lakes in central Ontario to changes in mean epilimnetic total phosphorus ({TPepi)} within a lake was highly variable between years during the period 1976–87. The linear regression coefficient of determination, R2, using all annual means was only 0.36, and within-lake regressions revealed mostly random associations between chlorophyll a and {TPepi.} Neverthless, by using the long-term average of annual means for each lake, a bivariate linear regression model was developed relating the long-term, average response of chlorophyll a to the long-term, average {TPepi} concentration in these lakes (R2 = 0.78). Annual variation could not be explained by changes in epilimnetic total nitrogen to total phosphorus ratio ({TN/TP).} The R2 increased slightly from 0.78 to 0.82 with {TN/TP} as a second independent variable using long-term averages but remained at 0.78 with {1/TPepi} as a second variable. Reanalysis of published data e..., La production moyenne de chlorophylle a (absence de glace) dans 15 lacs stratifiés mésotrophiques et oligotrophiques du centre de {l'Ontario} et dans lesquels la croissance des végétaux était limitée par le P montrait, de 1976 à 1987, d'importantes variations annuelles en fonction de la concentration de phosphore total épilimnétique ({PTépi).} Le coefficient de détermination de la régression linéaire, R2, établi à partir de toutes les moyennes annuelles était de seulement, 0,36 et les régressions par lac indiquaient que les relations entre chlorophylle a et {PTépi} étaient le plus souvent aléatoires. Néanmoins, au moyen de la moyenne à long terme des moyennes annuelles pour chaque lac, un modèle de régression linéaire à deux variables reliant la réponse moyenne à long terme de la chlorophylle a à la concentration moyenne à long terme du {PTépi} (R2 = 0,78) dans ces lacs a été élaboré. Les changements du rapport d'azote total à phosphore total ({NT/PT)} dans l'épilimnion ne permettent pas de rendre compte des variat...},
	number = {1},
	urldate = {2013-06-20},
	journal = {Canadian Journal of Fisheries and Aquatic Sciences},
	author = {Molot, Lewis A. and Dillon, P. J.},
	month = jan,
	year = {1991},
	pages = {140--145}
}

@article{moore_source_2011,
	title = {Source and Delivery of Nutrients to Receiving Waters in the Northeastern and Mid-Atlantic Regions of the United States1},
	volume = {47},
	copyright = {© 2011 American Water Resources Association. This article is a {U.S.} Government work and is in the public domain in the {USA}},
	issn = {1752-1688},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1752-1688.2011.00582.x/abstract},
	doi = {10.1111/j.1752-1688.2011.00582.x},
	abstract = {Moore, Richard B., Craig M. Johnston, Richard A. Smith, and Bryan Milstead, 2011. Source and Delivery of Nutrients to Receiving Waters in the Northeastern and Mid-Atlantic Regions of the United States. Journal of the American Water Resources Association ({JAWRA)} 47(5):965-990. {DOI:} 10.1111/j.1752-{1688.2011.00582.xAbstract:} This study investigates nutrient sources and transport to receiving waters, in order to provide spatially detailed information to aid water-resources managers concerned with eutrophication and nutrient management strategies. {SPAtially} Referenced Regressions On Watershed attributes ({SPARROW)} nutrient models were developed for the Northeastern and Mid-Atlantic ({NE} {US)} regions of the United States to represent source conditions for the year 2002. The model developed to examine the source and delivery of nitrogen to the estuaries of nine large rivers along the {NE} {US} Seaboard indicated that agricultural sources contribute the largest percentage (37\%) of the total nitrogen load delivered to the estuaries. Point sources account for 28\% while atmospheric deposition accounts for 20\%. A second {SPARROW} model was used to examine the sources and delivery of phosphorus to lakes and reservoirs throughout the {NE} {US.} The greatest attenuation of phosphorus occurred in lakes that were large relative to the size of their watershed. Model results show that, within the {NE} {US}, aquatic decay of nutrients is quite limited on an annual basis and that we especially cannot rely on natural attenuation to remove nutrients within the larger rivers nor within lakes with large watersheds relative to the size of the lake.},
	language = {en},
	number = {5},
	urldate = {2013-06-20},
	journal = {{JAWRA} Journal of the American Water Resources Association},
	author = {Moore, Richard B. and Johnston, Craig M. and Smith, Richard A. and Milstead, Bryan},
	year = {2011},
	keywords = {Phosphorus, {SPARROW}, nitrogen, nutrients, stochastic models, transport and fate},
	pages = {965–990}
}

@article{dokulil_cyanobacterial_2000,
	title = {Cyanobacterial dominance in lakes},
	volume = {438},
	url = {http://link.springer.com/article/10.1023/A%3A1004155810302},
	number = {1-3},
	urldate = {2013-06-20},
	journal = {Hydrobiologia},
	author = {Dokulil, Martin T. and Teubner, Katrin},
	year = {2000},
	pages = {1–12}
}

@article{paerl_controlling_2011,
	title = {Controlling harmful cyanobacterial blooms in a world experiencing anthropogenic and climatic-induced change},
	volume = {409},
	issn = {00489697},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0048969711001197},
	doi = {10.1016/j.scitotenv.2011.02.001},
	number = {10},
	urldate = {2013-06-20},
	journal = {Science of The Total Environment},
	author = {Paerl, Hans W. and Hall, Nathan S. and Calandrino, Elizabeth S.},
	month = apr,
	year = {2011},
	pages = {1739--1745}
}

@article{paerl_climate_2012,
	title = {Climate change: Links to global expansion of harmful cyanobacteria},
	volume = {46},
	issn = {00431354},
	shorttitle = {Climate change},
	url = {http://linkinghub.elsevier.com/retrieve/pii/S0043135411004386},
	doi = {10.1016/j.watres.2011.08.002},
	number = {5},
	urldate = {2013-06-20},
	journal = {Water Research},
	author = {Paerl, Hans W. and Paul, Valerie J.},
	month = apr,
	year = {2012},
	pages = {1349--1363}
}

@article{taranu_predicting_2012,
	title = {Predicting cyanobacterial dynamics in the face of global change: the importance of scale and environmental context},
	volume = {18},
	issn = {13541013},
	shorttitle = {Predicting cyanobacterial dynamics in the face of global change},
	url = {http://doi.wiley.com/10.1111/gcb.12015},
	doi = {10.1111/gcb.12015},
	number = {12},
	urldate = {2013-06-20},
	journal = {Global Change Biology},
	author = {Taranu, Zofia E. and Zurawell, Ron W. and Pick, Frances and Gregory-Eaves, Irene},
	month = dec,
	year = {2012},
	pages = {3477--3490}
}

@article{carvalho_cyanobacterial_2011,
	title = {Cyanobacterial blooms: Statistical models describing risk factors for national-scale lake assessment and lake management},
	volume = {409},
	issn = {0048-9697},
	shorttitle = {Cyanobacterial blooms},
	url = {http://www.sciencedirect.com/science/article/pii/S004896971101031X},
	doi = {10.1016/j.scitotenv.2011.09.030},
	abstract = {Cyanobacterial toxins constitute one of the most high risk categories of waterborne toxic biological substances. For this reason there is a clear need to know which freshwater environments are most susceptible to the development of large populations of cyanobacteria. Phytoplankton data from 134 {UK} lakes were used to develop a series of Generalised Additive Models and Generalised Additive Mixed Models to describe which kinds of lakes may be susceptible to cyanobacterial blooms using widely available explanatory variables. Models were developed for log cyanobacterial biovolume. Water colour and alkalinity are significant explanatory variables and retention time and {TP} borderline significant (R2-adj = 21.9\%). Surprisingly, the models developed reveal that nutrient concentrations are not the primary explanatory variable; water colour and alkalinity were more important. However, given suitable environments (low colour, neutral-alkaline waters), cyanobacteria do increase with both increasing retention time and increasing {TP} concentrations, supporting the observations that cyanobacteria are one of the most visible symptoms of eutrophication, particularly in warm, dry summers. The models can contribute to the assessment of risks to public health, at a regional- to national level, helping target lake monitoring and management more cost-effectively at those lakes at the highest risk of breaching World Health Organisation guideline levels for cyanobacteria in recreational waters. The models also inform restoration options available for reducing cyanobacterial blooms, indicating that, in the highest risk lakes (alkaline, low colour lakes), risks can generally be lessened through management aimed at reducing nutrient loads and increasing flushing during summer.},
	number = {24},
	urldate = {2013-06-20},
	journal = {Science of The Total Environment},
	author = {Carvalho, Laurence and Miller, Claire A. and Scott, E. Marian and Codd, Geoffrey A. and Davies, P. Sian and Tyler, Andrew N.},
	month = nov,
	year = {2011},
	keywords = {Algal bloom, Blue-green algae, Cyanotoxin, Phosphorus, Restoration, Water framework directive},
	pages = {5353--5358}
}

@article{milstead2013estimating,
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  journal={PloS one},
  volume={8},
  number={11},
  pages={e81457},
  year={2013},
  publisher={Public Library of Science}
}

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  author={USEPA},
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@Misc{GitHub,
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  title = {Facebook},
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  title = {Twitter},
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  title = {Research Blogs and the Discussion of Scholarly Information},
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  numeral = {1},
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  doi = {10.1371/journal.pone.0043235},
  number = {9},
  urldate = {2013-12-10},
  journal = {PLoS ONE},
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  pages = {e43235},
  numeral = {1},
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  doi = {10.1371/journal.pbio.1001687},
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  urldate = {2013-12-10},
  journal = {PLoS Biology},
  author = {Martin Fenner},
  month = {oct},
  year = {2013},
  pages = {e1001687},
  numeral = {1},
}
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  title = {Research Blogging: Indexing and Registering the Change in Science 2.0},
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  issn = {1932-6203},
  shorttitle = {Research Blogging},
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  doi = {10.1371/journal.pone.0050109},
  number = {12},
  urldate = {2013-12-10},
  journal = {PLoS ONE},
  author = {Sibele Fausto and Fabio A. Machado and Luiz Fernando J. Bento and Atila Iamarino and Tatiana R. Nahas and David S. Munger},
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  title = {Identifying Audiences of E-Infrastructures - Tools for Measuring Impact},
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  journal = {PLoS ONE},
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  editor = {Christos A. Ouzounis},
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  pages = {e50943},
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}
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}

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  title = {Tweeting the Meeting: An In-Depth Analysis of Twitter Activity at Kidney Week 2011},
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  shorttitle = {Tweeting the Meeting},
  url = {http://dx.plos.org/10.1371/journal.pone.0040253},
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@Article{Allen2013,
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  year = {2013},
  file = {Untitled Attachment:C\:\\Users\\jhollist\\AppData\\Roaming\\Zotero\\Zotero\\Profiles\\ocm21zg9.default\\zotero\\storage\\NEPBUEDC\\Federer - 2013 - Uses for Twitter across disciplines and throughout.html:text/html},
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@Article{Darling2013,
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  volume = {6},
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@Article{Fox2012,
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  doi = {10.4033/iee.v5i2.4457},
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  author = {Jeremy Fox},
  month = {nov},
  year = {2012},
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  number = {4},
  urldate = {2013-12-10},
  journal = {PLoS Biol},
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  month = {apr},
  year = {2013},
  pages = {e1001535},
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  numeral = {1},
}

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@article{jones2004importance,
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@article{jones2001predicting,
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@article{seilheimer2013landscape,
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  year={2013},
  publisher={Elsevier}
}

@article{cutler2007random,
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